COMPEL Glossary / data-lakehouse
Data Lakehouse
A data lakehouse is a modern data architecture that combines the flexibility and scale of a data lake with the management features, performance, and data governance capabilities of a traditional data warehouse.
What this means in practice
Lakehouses can handle both structured and unstructured data while providing the transaction support, schema enforcement, and query performance that enterprise AI workloads require. The lakehouse architecture is increasingly the preferred foundation for enterprise AI because it unifies analytics and ML workloads on a single platform, reducing data duplication and simplifying governance. In the COMPEL maturity model, lakehouse architecture typically appears at Level 3 in the Data Infrastructure domain (Domain 10), representing the transition from siloed storage to unified data platform architecture.
Why it matters
The data lakehouse architecture resolves the longstanding tension between the flexibility of data lakes and the governance capabilities of data warehouses. Organizations no longer need to maintain separate systems for analytics and AI workloads, reducing data duplication, simplifying governance, and lowering total cost of ownership. This convergence is increasingly the preferred foundation for enterprise AI platforms.
How COMPEL uses it
In the COMPEL maturity model, lakehouse architecture typically appears at Level 3 in the Data Infrastructure domain (Domain 10), representing the transition from siloed storage to unified data platform architecture. During the Model stage, lakehouse adoption is evaluated as a strategic technology decision within the Technology pillar, with implementation guided through the Produce stage and outcomes measured during Evaluate.
Related Terms
Other glossary terms mentioned in this entry's definition and context.